1. 1. Protein-Protein and Protein-Ligand Docking Abhijeet Kadam TSEC BioTechnology
2. 2. Defination Given two biological molecules determine: Whether the two molecules interact If so, what is the orientation that maximizes the interaction while minimizing the total energy of the complex Goal: To be able to search a database of molecular structures and retrieve all molecules that can interact with the query structure
3. 3. Example: HIV-1 Protease Docking to find drug candidates Active Site (Aspartyl groups)
4. 4. Difficulty in docking Number of possible conformations are astronomical thousands of degrees of freedom (DOF) Free energy changes are small Below the accuracy of our energy functions Molecules are flexible alter each others structure as they interact
5. 5. Types of Docking studies Protein-Protein Docking Both molecules usually considered rigid 6 degrees of freedom First apply steric constraints to limit search space and Next examine energetics of possible binding conformations Protein-Ligand Docking Flexible ligand, rigid-receptor Search space much larger Either reduce flexible ligand to rigid fragments connected by one or several hinges, or search the conformational space using monte-carlo methods or molecular dynamics
6. 6. Techniques of docking Surface representation, that efficiently represents the docking surface and identifies the regions of interest (cavities and protrusions) Connolly surface Lenhoff technique Kuntz et al. Clustered-Spheres Alpha shapes Surface matching that matches surfaces to optimize a binding score: Geometric Hashing
7. 7. Surface Representation Each atomic sphere is given the van der Waals radius of the atom Rolling a Probe Sphere over the Van der Waals Surface leads to the Solvent Reentrant Surface or Connolly surface
8. 8. Lenhoff technique Computes a complementary surface for the receptor instead of the Connolly surface, i.e. computes possible positions for the atom centers of the ligand Atom centers of the ligand van der Waals surface
9. 9. Kuntz et al. Clustered-Spheres Uses clustered-spheres to identify cavities on the receptor and protrusions on the ligand Compute a sphere for every pair of surface points, i and j, with the sphere center on the normal from point i Regions where many spheres overlap are either cavities (on the receptor) or protrusions (on the ligand)
10. 10. Alpha Shapes Formalizes the idea of shape In 2D an edge between two points is alpha-exposed if there exists a circle of radius alpha such that the two points lie on the surface of the circle and the circle contains no other points from the point set
11. 11. Alpha Shapes: Example Alpha=infinity Alpha=3.0
12. 12. Surface Matching Find the transformation (rotation + translation) that will maximize the number of matching surface points from the receptor and the ligand First satisfy steric constraints Find the best fit of the receptor and ligand using only geometrical constraints then use energy calculations to refine the docking Select the fit that has the minimum energy
13. 13. Geometric Hashing Building the Hash Table: For each triplet of points from the ligand, generate a unique system of reference Store the position and orientation of all remaining points in this coordinate system in the Hash Table Searching in the Hash Table Determine those entries that received more than a threshold of votes, such entry corresponds to a potential match For each potential match recover the transformation T that results in the best least-squares match between all corresponding triplets Transform the features of the model according to the recovered transformation T and verify it. If the verification fails, choose a different receptor triplet and repeat the searching.
14. 14. Example Docking Programs DOCK (I. D. Kuntz, UCSF) AutoDOCK (A. Olson, Scripps) RosettaDOCK (Baker, U Wash., Gray, JHU)
15. 15. DOCK DOCK works in 5 steps: Step 1Step 1 Start with coordinates of target receptor Step 2 Generate molecular surface for receptor Step 3 Fill active site of receptor with spheres potential locations for ligand atoms Step 4 Match sphere centers to ligand atoms determines possible orientations for the ligand Step 5 Find the top scoring orientation
16. 16. Other Docking programs AutoDock designed to dock flexible ligands into receptor binding sites Has a range of powerful optimization algorithms RosettaDOCK models physical forces Creates a large number of decoys degeneracy after clustering is final criterion in selection of decoys to output
17. 17. A Protein-Protein Docking Algorithm (Gray & Baker) Goal: to predict protein-protein complexes from the coordinates of unbound monomer components. Two steps: A - low-resolution Monte Carlo search and B - final optimization using Monte Carlo minimization. Up to 105 independent simulations produce decoys that are ranked using an energy function. The top-ranking decoys are clustered for output.
18. 18. Docking protocol
19. 19. Docking protocol: Step 1 RANDOM START POSITIONRANDOM START POSITION Creation of a decoy begins with a random orientation of each partner and a translation of one partner along the line of protein centers to create a glancing contact between the proteins
20. 20. Docking protocol: Step 2 LOW-RESOLUTION MONTE CARLO SEARCHLOW-RESOLUTION MONTE CARLO SEARCH Low-resolution representation: N, C , C, O for the backbone and a centroid for the side-chain One partner is translated and rotated around the surface of the other through 500 Monte Carlo move attempts The score terms: A reward for contacting residues, a penalty for overlapping residues, an alignment score, residue environment and residue-residue interactions
21. 21. Docking protocol: Step 3 HIGH-RESOLUTION REFINEMENTHIGH-RESOLUTION REFINEMENT Explicit side-chains are added to the protein backbones using a rotamer packing algorithm, thus changing the energy surface An explicit minimization finds the nearest local minimum accessible via rigid body translation and rotation Start and Finish positions are compared by the Metropolis criterion
22. 22. Docking protocol: Step 3 Before each cycle, the position of one protein is perturbed by random translations and by random rotations To simultaneously optimize the side-chain conformations and the rigid body position, the side-chain packing and the minimization operations are repeated 50 times
23. 23. Docking protocol: Step 3 COMPUTATIONAL EFFICIENCY The packing algorithm usually varies the conformation of one residue at a time; rotamer optimization is performed once every eight cycles Periodically filter to detect and reject inferior decoys without further refinement
24. 24. Docking protocol: Step 4 CLUSTERING & PREDICTIONSCLUSTERING & PREDICTIONS Repeat search to create approximately 105 decoys per target Cluster best 200 decoys by a hierarchical clustering algorithm using RMSD The clusters with the most members become predictions, ranked by cluster size
25. 25. Docking protocol: Results
26. 26. Problems in Docking The computational molecular docking problem is far from being solved. There are two major bottle-necks: The algorithms handle limited flexibility Need selective and efficient scoring functions